Electronic calendar data, to-do lists, and other user-specific data often identify particular locations. Existing systems identify the locations by place names such as the names of stores, restaurants, street addresses, or latitude/longitude coordinates. In some instances, however, the same location can have different meaning for the same user depending on factors such as the time of day. For example, a local coffee house may be the location where the user obtains breakfast in the morning and the same location where the user attends a book club meeting at night. Similarly, the user may attend a wedding reception at a local banquet hall on one day and then attend a baby shower at the same banquet hall another day. In this example, the location has multiple meanings for the same user. Existing systems fail to identify and distinguish between the different contexts that apply to the same location for a particular user.